- Michael Brady
- Kirti Rajagopalan
- Jennifer Adams
- Chad Kruger (CSANR)
Feb 04, 2018
How do we measure a causal effect?
An excellent review is Blanc and Reilly (2017).
Matching provides a method of balancing climate covariates \(Z\) between treatment and control groups.
If \(c(i)\) is the control that minimizes \(\min \limits_{j} d(X_i, X_j)\) for some distance metric \(d\), then a matching estimator is
\[\hat{\tau}_{1p} = \frac{1}{N_1} \sum \limits_{i \in N_1} (Y_{i,1} - Y_{c(i), 0}),\]
where \(Y_{i,1}\) is the treated observation and \(Y_{c(i),0}\) is the matched control observation (Imbens and Rubin 2015).
Propensity score matching: good at minimizing distance along a single dimension that is a combination of all covariates.
Mahalanobis distance matching: good at minimizing the sum of distances between individual covariates.
Genetic matching: search space over a weighted combination of the two (Sekhon 2011).
Decompose mechanisms specifically:
Blanc, Elodie, and John Reilly. 2017. “Approaches to Assessing Climate Change Impacts on Agriculture: An Overview of the Debate.” Review of Environmental Economics and Policy 11 (2). Oxford University Press (OUP): 247–57. doi:10.1093/reep/rex011.
Imbens, Guido W, and Donald B Rubin. 2015. Causal Inference in Statistics, Social, and Biomedical Sciences. Cambridge University Press.
Mendelsohn, Robert, William Nordhaus, and Daigee Shaw. 1994. “The Impact of Global Warming on Agriculture: A Ricardian Analysis.” The American Economic Review. JSTOR, 753–71.
Schlenker, Wolfram, and M. J. Roberts. 2009. “Nonlinear Temperature Effects Indicate Severe Damages to U.s. Crop Yields Under Climate Change.” Proceedings of the National Academy of Sciences 106 (37): 15594–8. doi:10.1073/pnas.0906865106.
Sekhon, Jasjeet S. 2011. “Multivariate and Propensity Score Matching Software with Automated Balance Optimization: The Matching Package for R.” Journal of Statistical Software 42 (7): 1–52.